Image Menu
Image processing functions
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Contents
Mode
Allows to change mode of the shown dataset, the following options are available:
- Grayscale, converts image to grayscale by removing any color information
- RGB Color, converts image to the RGB color space
- HSV Color, converts image to the HSV (hue, saturation, value) color space
- Indexed, converts image to the indexed colors (not implemented for True Color images)
- 8 bit, convert dataset to the 8 bit format, the image intensities are scaled to preserve the adjusted from the View Settings Panel->Display dialog
- 16 bit, convert dataset to the 16 bit format, the image intensities are scaled to preserve contrast of the original dataset
- 32 bit, convert dataset to the 32 bit format, the image intensities are scaled to preserve contrast of the original dataset
Color Channels
Perform some actions with color channels of the image
A brief demonstration is available in the following video:
https://youtu.be/gT-c8TiLcuY
- Insert empty channel..., insert an empty channel (intensity of all pixels is 0) to the specified position
- Copy channel..., copy one channel to another position
- Invert channel..., invert intensities of the specified color channel
- Rotate channel..., rotate the specified color channel
- Swap channels..., allows to swap two color channels between each other
- Delete channel..., deletes specified color channel from the dataset
It is also possible to do color channel operations from the Colors table in the View settings panel.
Contrast
Adjust contrast of the dataset. For the linear contrast stretching it is recommended to use Image Adjustment dialog available via the Display button in the View Settings panel.
A tutorial on image normalization is available in the following video:
https://youtu.be/MmBmdGtuUdM
- Linear contrast, linear contrast adjustment for current or all slices. It is however recommended to use the Display button in the View settings panel.
- Contrast-limited adaptive histogram equalization, CLAHE contrast equalization. CLAHE operates on small regions in the image, called tiles, rather than the entire image. Each tile's contrast is enhanced, so that the histogram of the output region approximately matches the histogram specified by the 'Distribution' parameter. The neighboring tiles are then combined using bilinear interpolation to eliminate artificially induced boundaries. The contrast, especially in homogeneous areas, can be limited to avoid amplifying any noise that might be present in the image. For details see documentation for Matlab function adapthisteq.
- Normalize layers, normalization of image intensities between the slices. A) calculates mean intensity and standard deviation (std) for the whole dataset; B) calculates mean intensities and standard deviation for each image; C) shifts each image based on difference between mean values of that image and the whole dataset, plus stretches it based on ratio between standard deviation of the whole dataset and each image. For the 4D datasets it is possible to perform normalization also via the time dimension.
- Normalize layers based on masked areas, essentially similar to the Normalize layers mode, except that all values are calculated from the masked areas only.
- Normalize based on masked background, normalizes image intensities between the slices using background areas that should be masked. A) calculates mean intensity for the masked area for the whole dataset; B) calculates mean intensities for the masked area for each image; C) shifts each image based on difference between mean values of the image and the whole dataset
Invert image
Invert image intensities, shortcut Ctrl+i.
A brief demonstration is available in the following video:
https://youtu.be/1DG2w5XYA18
- Shown slice (2D), invert only the currently shown slice of the dataset
- Current stack (3D), invert the current stack of the dataset
- Complete volume (4D), invert complete dataset
Morphological operations
This section contains number of morphological operations that can be applied to images. The processed image may be also added or subtracted from the existing image (see the settings in the Additional action to the result panel).
A brief demonstration is available in the following video:
https://youtu.be/itbVLFm0FKQ
List of available morphological operations
- Bottom-hat filtering (imbothat) computes the morphological closing of the image (using imclose`) and then subtracts the result from the original image
- Clear border (imclearborder) suppresses light structures connected to image border
- Morphological closing (imclose) morphologically closes the image: a dilation followed by an erosion
- Dilate image (imdilate)
- Erode image (imerode)
- Fill regions (imfill) fills holes in the image, where a hole is defined as an area of dark pixels surrounded by lighter pixels
- H-maxima transform (imhmax) suppresses all maxima in the image whose height is less than H
- H-mminima transform (imhmin) uppresses all minima in the image whose depth is less than H
- Morphological opening (imopen) morphologically opens image: an erosion followed by a dilation
- Top-hat filtering (imtophat) computes the morphological opening of the image (using imopen) and then subtracts the result from the original image
Intensity profile
Generate an intensity profile of the image data. The profiles can be obtained in two modes:
- Line
- Arbitrary
For intensity profiles it is recommended to use the Measure length tool.
Histogram
Show histogram of image intensities, see details in imcontrast function of Matlab.
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